*This seminar is cross listed from the Department of Statistics, Colloquium. PLEASE NOTE DIFFERENT TIME AND DAY: OCTOBER 19TH, 4-5 PM, 201 THOMAS.*
We present a novel approach for improving particle filters for multi-target tracking with a nonlinear observation model. The suggested approach is based on simulated annealing for stochastic differential equations. Simulated annealing is used to design a Markov Chain Monte Carlo step which is appended to the particle filter and aims to bring the particle filter samples closer to the observations while at the same time respecting the dynamics. We employ a similar algorithm for non-Gaussian dynamics and data. The numerical results based on two tracking scenarios show that the suggested approach can improve significantly the performance of a particle filter.
The talk is based on joint works with Kai Kang and Panos Stinis.